rust-timeseries 0.1.7

Rust-implemented time-series utilities exposed to Python via PyO3.

rust_timeseries

PyPI CI

rust_timeseries is a high-performance Python library for time-series diagnostics.
The first release implements the Escanciano–Lobato (2009) robust automatic portmanteau test for serial dependence.
Heavy lifting is handled in Rust (via PyO3) so you get C-level speed with a pure-Python interface.

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✨ Highlights

Why it matters	What you get
Native-code core	Tight Rust loops compiled to machine code
Zero-copy I/O	numpy.ndarray / pandas.Series buffers are viewed directly—no copying ever
Heteroskedastic-robust	τ̂-adjusted autocorrelations maintain validity under conditional heteroskedasticity
Automatic lag choice	Data-driven (p̃) maximises the penalised statistic (L_p)
Friendly errors	Clear ValueError / OSError when inputs are invalid

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📦 Installation

pip install rust_timeseries

Binary wheels are provided for Python 3.9–3.13 on Linux x86-64, macOS (Intel & Apple Silicon) and Windows 64-bit.
If no wheel matches your platform a source install will build automatically—just have Rust 1.76+ on PATH.

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🚀 Quick start

import rust_timeseries as rts
import numpy as np

y = np.random.randn(500)

test = rts.statistical_tests.EscancianoLobato(y, q=3.5)  # d defaults to ⌊n**0.2⌋
print(f"Q*      = {test.statistic:.3f}")
print(f"p̃       = {test.p_tilde}")
print(f"p-value = {test.pvalue:.4f}")

API snapshot

Object	Attribute	Meaning
EscancianoLobato	.statistic	Robust Box–Pierce statistic (Q^{*}_{p̃})
	.pvalue	Asymptotic χ² (1) tail probability
	.p_tilde	Data-driven lag (p̃)

Constructor signature

EscancianoLobato(data, /, *, q=2.4, d=None)

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⚙️ How it works

All numerics live in safe Rust (src/), compiled into a shared library and imported by Python.
The Rust crate is internal; no stable Rust API is promised.

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🛠 Development setup

git clone https://github.com/your-org/rust_timeseries
cd rust_timeseries
python -m venv .venv && source .venv/bin/activate
pip install -U pip maturin
maturin develop --features pyo3/extension-module
pytest

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📜 License

Released under the MIT License – free for commercial and academic use.

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📖 Reference

Escanciano, J. C. & Lobato, I. N. (2009). Testing serial correlation in time series with missing observations. Journal of Econometrics 150, 209–225.